Determine the subgroup effects — RSAVS_Determine

This function determines the final mu vector given the grouping results.

RSAVS_Determine_Mu(
  mu_vec,
  group_res,
  klim = c(2, 7, 4),
  usepam = (length(mu_vec) < 2000),
  round_digits = NULL
)

Arguments

mu_vec: numerical vector. n = length(mu_vec) is the number of observations. Presumably it comes from the ADMM algorithm and is not a very good grouping result.
group_res: a list, containing the grouping results. Each element of group_res is a list containing the indecs from the same subgroup. See RSAVS_S_to_Groups for the output structure.
klim: a length-3 integer vector, storing `kmin`, `kmax`, `dudahart_kmax` for specifying settings for clustering algorithm.
usepam: boolen, whether to use `cluster::pam()` or instead `cluster::clara()` to estimate center of clusters.
round_digits: a positive integer digit. If group_res is missing and round_digits is provided, then the function will directly round the mu_vec to round_digits to provide the final grouping results.

Value

a length-n vector of subgroup effects. Observations belonging to the same subgroup should have the same effects.

Details

Currently, the resulting subgroup effects are the average value of mu_vec for those belonging to the same subgroup according to group_res.

If group_res is missing and round_digits is NULL, then a simple K-means is performed on mu_vec to get the estimated grouping result.

Examples

# basic settings
set.seed(1024)
n <- 100    # number of observations
group_center <- c(-1, 0, 1)    # three group centers
# subgroup effect vector    
alpha_true <- sample(group_center, size = n, replace = TRUE)
alpha_est <- alpha_true + rnorm(n, sd = 0.25)    # add noise to group effect
table(alpha_true)
#> alpha_true
#> -1  0  1 
#> 36 29 35 

# Use `group_res` to determine estimated group effect from `alpha_est`
d_mat <- RSAVS:::RSAVS_Generate_D_Matrix(n)    # pair-wise difference matrix
# Normally, s_vec should come from the algorithm
#   Here we just use a simple estimate, which might lead to not so satisfing results
s_vec <- round(d_mat %*% alpha_est, 0)    
group_res <- RSAVS:::RSAVS_S_to_Groups(s_vec, n)
table(RSAVS:::RSAVS_Determine_Mu(alpha_est, group_res = group_res))
#> 
#> -0.031212280735415 
#>                100 

# Use `pamk` to determine estiamted group effect
table(RSAVS:::RSAVS_Determine_Mu(alpha_est))
#> 
#>    -0.93070469388076 -0.00378625575910929      1.0149849134033 
#>                   37                   29                   34 

# Use directly rounding to determine estimated group effect
table(RSAVS:::RSAVS_Determine_Mu(alpha_est, round_digits = 1))
#> 
#> -1.5 -1.4 -1.3 -1.2 -1.1   -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.3 -0.2 -0.1    0  0.1 
#>    2    1    4    5    2    3    8    8    1    1    3    1    3    6    8    4 
#>  0.2  0.3  0.4  0.6  0.7  0.8  0.9    1  1.1  1.2  1.3  1.6 
#>    3    2    1    1    5    3    6    5    6    3    4    1